4 citations · 10 across the 14 of their papers we have counts for
6 papers · 1 filter
Training Mixed-Monotone Neural Networks for Verification Without Set Propagation
Jonas Fingerhut, Lukas Koller, Matthias Althoff
Most formal verification approaches for neural networks conservatively enclose the output set of the neural network. However, computing a tight output enclosure is in general compu…
The Misery of Mechanistic Interpretability: A Formal Perspective
Tobias Ladner, Matthias Althoff
Mechanistic interpretability has become the dominant lens for understanding frontier language models, as their inner workings are complex and inherently black boxes. To gain insigh…
Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems
Miriam Kranzlmüller, Lukas Koller, Tobias Ladner +1
Barrier certificates are scalar functions over the state space of dynamical systems that separate all unsafe states from all reachable states. The existence of a barrier certificat…
Provably Explaining Neural Additive Models
Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +4
Despite significant progress in post-hoc explanation methods for neural networks, many remain heuristic and lack provable guarantees. A key approach for obtaining explanations with…
Perception with Guarantees: Certified Pose Estimation via Reachability Analysis
Tobias Ladner, Yasser Shoukry, Matthias Althoff
Agents in cyber-physical systems are increasingly entrusted with safety-critical tasks. Ensuring safety of these agents often requires localizing the pose for subsequent actions. P…
BSAT: B-Spline Adaptive Tokenizer for Long-Term Time Series Forecasting
Maximilian Reinwardt, Michael Eichelbeck, Matthias Althoff
Long-term time series forecasting using transformers is hampered by the quadratic complexity of self-attention and the rigidity of uniform patching, which may be misaligned with th…